Public support for more stringent vaccine policies increases with vaccine effectiveness

Under what conditions do citizens support coercive public policies? Although recent research suggests that people prefer policies that preserve freedom of choice, such as behavioural nudges, many citizens accepted stringent policy interventions like fines and mandates to promote vaccination during the COVID-19 pandemic—a pattern that may be linked to the unusually high effectiveness of COVID-19 vaccines. We conducted a large online survey experiment (N = 42,417) in the Group of Seven (G-7) countries investigating the relationship between a policy’s effectiveness and public support for stringent policies. Our results indicate that public support for stringent vaccination policies increases as vaccine effectiveness increases, but at a modest scale. This relationship flattens at higher levels of vaccine effectiveness. These results suggest that intervention effectiveness can be a significant predictor of support for coercive policies but only up to some threshold of effectiveness.


Robustness Checks on Policy Support Index
Below we present estimates based on a series of item response theory (IRT) one-parameter logistic models for Study 1 and Study 2 data respectively.These are based on the four policy items in Study 1, and the seven policy items in Study 2. Responses to each item are coded 1 if they are associated with higher levels of policy stringency, and 0 otherwise.To ensure the estimates derived from this model are directly comparable to that of main analysis, we performed a listwise deletion of observations for which at least one policy stringency item was not answered.Predictions from these models are used to generate a measure of latent attitudes to policy stringency (theta), which in turn provides an alternative weighting of the various items associated with policy stringency.The scale of this alternative weighting can be interpreted in terms of standard deviations from the mean of the latent attitude.
The Test Characteristic Curve links values of the latent attitude (x-axis) to the summative score of survey responses on stringent policy items.The Item Characteristic Curve shows the probability of answering each item with the outcome positively associated with policy stringency for varying levels of theta.The further to the right a particular curve, the higher the level of discrimination associated with the associated item.In other words, the further to the right a particular curve, the greater the "difficulty" of the associated item.This in turn means, positive responses to items with higher levels of discrimination are more indicative of stringent attitudes as a whole.Using these weightings, we create an alternative index of support for stringent policy.The following tables replicate our main analyses -Table 3 for Study 1 and Table 6 for Study 2using the respective measures of policy support generated by the two IRT models described above.For a direct comparison with the results derived from the summative measure of stringent attitudes, we provide the results in Tables A7 and A8 rescaled in terms of standard deviations from the mean.The comparison demonstrates the results are robust to an alternative weighting of policy stringency items.Column 1 presents the results of our original and pre-registered analysis (using the summative index) in terms of standard deviations from the mean.Column 2 presents the results of IRT analysis (using the latent variable index) in terms of standard deviations from the mean.Model specification as in Table 3. *** p<0.005, ** p<0.01, * p<0.05.Column 1 presents the results of our original and pre-registered analysis (using the summative index) in terms of standard deviations from the mean.Column 2 presents the results of IRT analysis (using the latent variable index) in terms of standard deviations from the mean.Model specification as in Table 6.*** p<0.005, ** p<0.01, * p<0.05.

Table A2 .
ITT effect when the treatment is modelled as a continuous variable (Study 1 in Column 1 and Study 2 in Column 2).
OLS estimates with heteroskedasticity-robust standard errors in parentheses.We use randomization-t p-values 20 to account for multiple comparisons: *** p<0.005, ** p<0.01, * p<0.05.Controls selected by lasso linear regression specification19to increase the precision of our treatment effect estimates from our pre-registered list of covariates: age, gender, education, parental status, town/city type, religious beliefs, political left-right scale, risk preference, previous COVID-19 infection (self), previous COVID-19 infection (household), vaccination status, booster status, and trust in vaccines (binary).The data are unweighted.

Table A3 .
ITT effect on support for individual policies when booster effectiveness is modelled as a categorical variable -"50% effective" as the baseline category (Study 1) booster status, and trust in vaccines (binary).The data are unweighted.

Table A4 .
Summary statistics by treatment condition (Study 2) The data were collected from respondents living in seven democratic and high-income countries, namely Canada (N=2,566), France (N=2,623), Germany (N=2,568), Italy (N=2,565), Japan (N=2,674), UK (N=2,552), and US (N=2,566) from March to May 2022.University-educated refers to holding a Bachelor's or higher.As 77 respondents identified as non-binary, a different gender or preferred not to say, the N for Male is lower than the N for Age and University-educated.

Table A5 .
ITT effect when booster effectiveness is modelled as a categorical variable -"equally effective" as the baseline category (Study 2).
increase the precision of our treatment effect estimates from our pre-registered list of covariates: age, gender, education, parental status, town/city type, religious beliefs, political left-right scale, risk preference, previous COVID-19 infection (household), vaccination status, booster status, and trust in vaccines (binary).The data are unweighted.

Table A6 .
ITT effect on support for individual policies when booster effectiveness is modelled as a categorical variable -"less effective" as the baseline category (Study 2).
OLS estimates with heteroskedasticity-robust standard errors in parentheses.We use randomization-t p-values 20 to account for multiple comparisons: *** p<0.005, ** p<0.01, * p<0.05.Controls selected by lasso linear regression specification19to increase the precision of our treatment effect estimates from our pre-registered list of covariates: age, gender, education, parental status, town/city type, religious beliefs, political left-right scale, risk preference, age, gender, parental status, town/city type, religious beliefs, previous COVID-19 infection (self), previous COVID-19 infection (household), vaccination status, booster status, and trust in vaccines (binary).The data are unweighted.

Table A7 .
Study 1 Replication of main results with IRT index of latent support for stringent policy

Table A8 .
Study 2 IRT Replication of main results with IRT index of latent support for stringent policy